Image scanners, also known as document scanners, convert a visible image on a document or photograph, or an image in a transparent medium, into an electronic form suitable for copying, storing or processing by a computer. An image scanner may be a separate device, or an image scanner may be a part of a copier, part of a facsimile machine, or part of a multipurpose device. Reflective image scanners typically have a controlled source of light, and light is reflected off the surface of a document, through an optics system, and onto an array of photosensitive devices. Transparency image scanners pass light through a transparent image, for example a photographic positive slide, through an optics system, and then onto an array of photosensitive devices. The optics system focuses at least one line, called a scanline, on the image being scanned, onto the array of photosensitive devices. The photosensitive devices convert received light intensity into an electronic signal. An analog-to-digital converter converts the electronic signal into computer readable binary numbers, with each binary number representing an intensity value.
In general, there is an ongoing demand for increased resolution and speed, improved color quality and image quality, and reduced cost, demands that often directly conflict and require trade-offs. The following background presents some of the factors affecting color, resolution, speed, image quality and cost.
First, consider psychophysical color matching (i.e., colors that appear to be the same to the human eye). The human eye contains three different kinds of color receptors (cones) that are sensitive to broad overlapping spectral bands. Specific sensitivities vary from person to person, but the average response for each receptor has been quantified and is known as the “CIE standard observer.”
Typically, given a set of numerical values for photosensor responses for one pixel, for example, red, green, and blue, the numbers are mathematically treated as a vector. The vector is multiplied by a color transformation matrix to generate a different set of numbers. In general, the coefficients in the color transformation matrix compensate for differences between the response of photosensors and the response of the CIE standard observer, and the coefficients in the matrix may include compensation for the spectrum of the illumination source.
Metamers are visually identical, but spectrally different, stimuli. That is, it is possible to provide psychophysical color matching even though the color spectra are not identical. Measuring the actual spectrum, as opposed to just psychophysical color matching, provides additional data for more accurate color matching between systems that have different gamuts, for example color matching between displays and printers.
Accurate spectral reproduction requires a light source that has adequate intensity across the spectral bandwidth of the human eye, and photosensors with an adequate sensitivity across the spectral bandwidth of the human eye, and a display or printing system with adequate intensity across the spectral range of the human eye. Typically, light sources, photosensor arrays, displays, and printers all have limited color gamuts, and more particularly, all have spectral ranges that are narrower than the broad band spectral range of the human eye. One solution for light sources is to provide multiple light sources, each with a different spectral range (see, for example, U.S. Pat. No. 5,753,906). A similar solution for two-dimensional photosensor arrays for digital cameras is to provide additional photosensors with different sensitivity ranges. For example, if the photosensors that are sensitive to green light have an inadequate range of spectral sensitivity, then multiple different green sensitive photosensors may be used, each having a different spectral range (see, for example, U.S. Pat. No. 5,889,554). Typically, however, an additional spectral range increases cost (additional photosensors), or decreases resolution for one of the other colors (for example, green photosensors may be physically located where red or blue photosensors would normally be physically located).
Photosensor arrays typically have thousands of individual photosensitive elements. Each photosensitive element, in conjunction with the scanner optics system, measures light intensity from an effective area on the document defining a picture element (pixel) on the image being scanned. Optical sampling rate is often expressed as pixels per inch (or mm) as measured on the document (or object, or transparency) being scanned. Optical sampling rate as measured on the document being scanned is also called the input sampling rate.
The native input sampling rate is determined by the optics and the pitch of the individual sensors. A scanner operator may select a sampling rate that is less than the native input sampling rate by simply dropping selected pixels, or by using digital resampling techniques. Alternatively, a scanner operator may select a sampling rate that is greater than the native input sampling rate, where intermediate values are computed by interpolation. Typically, all the charges or voltages are read from the photosensor array, and are then digitized, and then subsampling or interpolation is performed on the resulting digital pixel data.
Bit-depth is the number of bits captured per pixel. Typically, a pixel is specified in a three-dimensional color space with a fixed number of bits in each dimension. For example, a pixel may be specified in red, green, blue (RGB) color space, with 8 bits of red information, 8 bits of green information, and 8 bits of blue information, for a total bit-depth of 24 bits per pixel. Alternatively, a pixel may be specified in a cylindrical color space in which the dimensions are luminance, chrominance, and saturation. Alternatively, a three-dimensional CIE color space may be used. Transformation matrices are used to transform between color spaces.
Even if a sensor is receiving no light, some thermal noise (called dark noise) may occur. Thermal noise (dark noise) is proportional to time. During exposure to light, the primary noise source (called shot noise) is related to conversion of photons to electrons, and the noise increases with the square root of the signal. Small sensors tend to have a lower signal-to-noise ratio than large sensors, particularly for low reflectance or low transmissivity areas of a document. Smaller sensor areas can provide higher input sampling rates, but other measures of image quality, and in particular color quality, as measured by signal-to-noise, may be reduced.
Scanning speed is affected by multiple factors: exposure time, shift time of registers multiplied by number of pixels being shifted, output amplifier speed, and analog-to-digital conversion time. Typically, for low native input sampling rates, the primary limiter is exposure time, that is, the time required to generate a signal that provides an acceptable signal-to-noise ratio. However, if the number of pixels being shifted and converted becomes very large, then the time required to shift and convert the individual pixel signals may become the limiting factor.
Areas of an image with slowly varying color, particularly dark colors, require high bit-depth and high signal-to-noise to accurately reproduce the smooth tone and texture of the original. For areas of slowly varying color, high input sampling rate is not needed because there is no high frequency information in the image. Areas of an image that change color rapidly, for example a forest scene, or a close-up photograph of a multi-colored fabric, need a high input sampling rate to capture the high frequency information, but high bit-depth and high signal-to-noise are not needed. That is, for high frequency information, the color accuracy of each individual pixel is less important. High input sampling rates require small sensor areas, which in turn have relatively low signal-to-noise ratios, relatively low bit-depth, and relatively low scanning speed. Large sensor areas provide high signal-to-noise, high bit-depth, and high speed, but cannot provide high input sampling rates.
There is a need for a scanner that provides accurate spectral reproduction, high bit-depth, high speed, high signal-to-noise, and high native input sampling rate, with minimal increase in cost.